The AI workforce: What is it? How do you build it?

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"Agents are coming. In the next few years, they will utterly change how we live our lives, online and off."


Bill Gates,
Founder of Microsoft

For years, AI has been hyped as the future of work. Thanks to the recent rise of AI agents—autonomous systems that can think and solve complex problems—the era of the AI workforce is finally here.

What is the AI workforce? It's a team of specialized AI agents that work together, using tools and real-time data to execute complex tasks as part of a multi-agent system. These agents work autonomously or collaboratively with humans to streamline operations and automate workflows, promising a new standard of efficiency, productivity, and innovation for business leaders.

With adoption of AI agents increasing, the AI workforce is a fast-growing segment of the labor pool. In fact, Gartner predicts AI agents will make 15% of all routine work decisions by 2028. As we transition to a future where an AI workforce works in tandem with humans, there’s a lot to understand.

This article highlights the strategies, benefits, and challenges of transitioning to the AI workforce so you can thrive in the fast-approaching era of AI.

How to build an AI workforce

To understand the true potential of the AI workforce, it helps to have a basic sense of how AI agents operate.  At a high level, the AI workforce is made up of a four main elements:

  1. AI agents: These are digital workers of the AI workforce. They have the ability to perceive their environment, iteratively solve problems, and learn from experience in pursuit of predefined goals. Agents are typically built on large language models (LLMs), which gives them the ability to understand and interact using natural language, while providing a core of general knowledge.

    AI agent

  2. Their environment: This includes all the systems, data, context, and spaces that AI agents are programmed to perceive and interact with. For instance, a customer service AI agent could retrieve CRM data, perform web searches, or refer to company FAQs. Once integrated with existing systems, agents can use any available components in their environment to reach their goal.

  3. Their tools: AI agents can use various tools, which meaningfully expands the functionality, intelligence, and adaptability of the AI workforce. Tools can includes APIs to retrieve real-time data like weather or stock market information, software integrations, or even mutli-step AI agentic workflows

  4. The multi-agent system (MAS): This type of AI architecture enables a group of AI agents to communicate, collaborate, and share data and outputs to achieve a common goal. Each autonomous agent is tailored to a specific function, so together, they can tackle complex, dynamic challenges that exceed the capabilities of a single agent. 

    Multi agent system

Key capabilities of the AI workforce

The AI workforce has emerged as near-term reality for organizations due to a few recent advancements in AI technology. These new capabilities far exceed those of traditional automation and generative AI tools, including:

Autonomous action

Combining goal-oriented behavior with advanced machine learning, AI agents execute tasks without intervention—even breaking complex tasks into smaller, manageable subtasks to reach completion. This capacity for autonomy, proactivity, and iterative-problem solving empowers the AI workforce to execute multi-step workflows that earlier AI could not.

Real-time data and tool use

Unlike GenAI models that are limited to their static knowledge base, AI agent systems can connect to external data sources and tools to execute tasks. This enables agents to make context-aware choices in real-time scenarios like customer service or financial trading, making the AI workforce into adaptive problem-solvers that act based on up-to-date information from any source.

Domain-specific intelligence

Vertical AI agents are a recent breakthrough that brings new levels of precision, reliability, and performance to the AI workforce. Unlike general-purpose AI that's trained on general information, vertical AI agents are custom-made with domain-specific intelligence to adress a specific task or function. With a team of specialized agents fine-tuned to a company's products, customers, regulatory requirements, and other key specifics, the possibility of end-to-end AI workflow automation becomes very real.

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Types of agents in the AI workforce

An effective AI workforce is a well-orchestrated AI system in which a team of specialized AI agents are equipped with relevant tools and collaborate toward a common goal. This means each business will have a unique set of AI agents, customized to their specific needs and workflows.

Three main types of AI agent systems can be used, depending on the structure, scope, and complexity of the workflow or application at hand:

1. Task-specific agents

An AI agent that focuses on one particular job within the larger system. Designed to handle a specific function or solve a narrowly defined problem within a particular domain, task-specific agents excel at executing well-defined tasks with precision and efficiency. Often, these tasks do not involve decision-making. Instead, the agent might delegate tasks to sub-agents, retrieve data, or verify outputs from other agents for accuracy. 

Example: A task-specific agent that routes customer support queries to other agents based on priority.

AI agent

2. Multi-agent systems (MAS)

A multi-agent system (MAS) is a collection of autonomous agents that collaborate to solve a set of interconnected problems or achieve a shared goal. Commonly used for multi-step workflows, multi-agent systems act as distributed modules that work together by communicating and coordinating tasks, offering scalability and adaptability in complex workflows. For instance, an MAS can involve a lead agent that delegates subtasks to other agents, then integrates their outputs and ensures contextually accurate and compliant outputs. 

Example: Financial portfolio management. When queried about a customer’s portfolio performance, market risks, and investment opportunities, the orchestrator agent splits the query in subtasks, assigning sub-agents to access the portfolio database, risk assessment tools, and market APIs, then combines the results into a personalized investment report.

Multi agent system of AI agents

3. Human-augmented agents

This is an agent system designed to collaborate with humans by automating complex tasks while incorporating human oversight, feedback, or decision-making.

As AI technology continues to advance and become increasingly integrated with business processes, this human-AI collaboration will serve as the starting point for many businesses as they augment human productivity.

Depending on application needs, human-augmented agent systems can include:

  • Humans-in-the-loop (HITL) agents: These agents integrate human feedback to validate, refine, or override their outputs and decisions to ensure contextual accuracy or compliance. This combines the benefits of automation with human judgment and expertise, often used in high-stakes applications like finance or healthcare.

  • Supervisory agent: The agent monitors processes, flags anomalies, and recommends corrective action for human validation or intervention. Example: agent monitors network traffic for cybersecurity threats.

  • Collaborative agent: The agent interacts with humans in real-time to provide insights, suggestions, and assist in task execution within predefined boundaries.

Example: Microsoft Co-pilot for coding is a collaborative AI agent. It's embedded directly into the Microsoft suite to help users create PowerPoint or Docs more efficiently.

Human-augmented agents
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How to build an AI agent: 5 key components

It takes a considered and careful approach to build an AI agent. This involves combining various AI technologies like machine learning and natural language processing and even building your own models, requiring an expert team of machine learning engineers, data scientists, and software engineers to develop, design, train, and manage the AI agent. By carefully designing and training the AI agent, developers can create a powerful tool that streamlines operations, automates tasks, and enhances decision-making.

There are five key components:

Large Language Model (LLM)

AI agents are built on a powerful foundational model, typically a large language model like GPT4. Trained on vast amounts of data, the LLM is the “brain” of an AI agent. It enables them to understand, generate, and respond to users in meaningful and nuanced language, which simplifies how teams interact with agents.

In addition to providing the AI workforce with cognitive and language capabilities, the LLM also serves as the orchestrator for agents, drivign how they think and act toward a task. Custom, domain-specific LLM models are custom-developed and fine-tuned to serve as the core of vertical AI agents.

System prompt

The system prompt is the set of overarching instructions that define how an AI agent should behave. This blueprint for action provides the reasoning and decision-making patterns for agents, as well as their personality. A basic example of a system prompt for an AI customer service agent is: 

"You are a friendly and informative customer service agent for a tech company. Respond to user inquiries with clear explanations, provide relevant product details, and always strive to be helpful."

System prompts must be carefully engineered to facilitate an appropriate response from the LLM, especially in the context of multi-agent systems with dynamic environments. Different prompts allow for different types of reasoning, such as chain of thought, tree of thought, or ReAct.

Memory

AI agents have a memory module that enables them to act intelligently and adapt over time. Memory allows agents to store and retrieve past experiences, maintain context between user interactions, and create more personalized experiences. For example, an AI customer service agent can account for current conversational context and customer history to determine the most appropriate course of action. 

Memory also enables agents to learn and adapt to evolving circumstances. While moving data in short-term memory to its knowledge base, the agent can reflect on its actions and outcomes, and adjust its decision-making accordingly.

Reflection and feedback

AI agents have the ability to self-reflect on interactions to enhance their performance and adaptability. By evaluating their own outputs and decision-making processes, they can adapt their strategies through a process of continuous learning that enables them to better handle dynamic environments over time.

Similarly, agents can receive feedback from users that can be incorporated into their core model using methods like reinforcement learning from human feedback (RLHF). 

Tools and integrations

AI agents need access to tools and data sources to function effectively. This is because AI agents base their decision-making on what they perceive in their environment, and when they find their available resources lacking, they access new data or functionality required to execute a task.

Tool use effectively expands the functionality and adaptability of the AI workforce, enabling it to solve complex problems that exceed the information in its knowledge base.

Tools for AI agents fall into three buckets:

  • Informational tools: Includes internal resources like FAQs and product documentation, as well as knowledge bases, web searches, or APIs for specific data sources like news or stock prices.

  • Functional tools: APIs or software integrations that run a particular action, such as “send an email” or “schedule meeting.”

  • Custom AI workflows: Typically made up of multiple domain-specific LLMs each with an expertly crafted system-prompt, custom workflow empower agents with specialized functions.

AI agent components


Multi-agent systems and the AI workforce

As with human teamwork, an effective AI workforce employs the expertise of different specialists to tackle complex challenges. For example, to launch a marketing campaign would require a content writer to create ad copy, a designer to design graphics, and a campaign manager to track performance and optimize to ensure a good outcome. If you only had a content writer, the design quality and data feedback would be lacking, and the campaign would flounder for its limitations.

Likewise, multi-agents systems enable a team of specialized agents to communicate, share data, and share their outputs to tackle challenges that exceed the capabilities of a single agent. New agents can be added or removed as needed, allowing for easy scaling of operations. Multi-agent systems require AI orchestration, which enables the various AI agents, tools, and systems to share data and work together effectively.

These systems can be developed using various design patterns that provide the ideal orchestration for a specific application. For example, in the graphic below, each black dot represents an AI agent as part of a multi-agent system:

Multi-agent systems and the AI workforce

Key stages to developing the AI workforce

The AI workforce landscape continues to evolve rapidly, with 75% of organizations saying have already deployed or plan to deploy co-pilot agents in the next year. To remain competitive means moving from where you are now to having your first agent, to an entire AI workforce.

There’s more to it than building models, and here’s a roadmap to guide your development and implementation strategy:

Steps to build an AI agent
  1. Define problems and goals: Clearly define the problem you want AI to solve, outlining its desired capabilities and the environment it will operate in.
  2. Prepare your data: The lifeblood of AI agents is data. Gather relevant data from various sources, then clean, label, and structure it to facilitate high-quality training for the agent.
  3. Design your agent: Select a suitable architecture for the agent’s applications, and machine learning model that are suitable for your application.
  4. Train your agent: Using the preprocessed training data, train the AI agent on examples in the data so it can perform tasks on its own.
  5. Test the agent: Evaluate the agent’s performance in various simulated environments to identify potential issues and refine its decision making process.
  6. Integrate with systems: Connect the developed agent to existing infrastructure that enables seamless interaction with other systems and data sources.
  7. Deploy and monitor: Once integrated, you’ll be able to see how the agent interacts with users and performs in the real-world. Regularly monitor the agent's performance, collect feedback, and update the model to adapt and improve over time.

A deeper dive: How to build an AI agent: The 8 key steps

How to structure and organize AI in the workforce

Integrating AI into the workforce isn’t just a technological upgrade—it’s a transformational shift in how businesses operate. Rather than trying to “bolt on” AI to existing structures and workflows, AI should be incorporated as a core capability into the workforce.

This requires an integrated approach in which AI isn’t siloed off. Rather, each team integrates AI into its workflows and objectives by use case, and in alignment with a larger strategic vision. Each AI project is linked to a business objective, and AI professionals should be hired and placed across the organizational chart, rather than centralized in one unit.

For example, a common pitfall to avoid is creating a central group of AI professionals and leaders that are loaned out to different departments on request (as with IT). Instead, AI talent should be accountable for business needs, and therefore should exist across the business as a basic unit of operations.

However, AI professionals and non-technical teams should have access to a centralized one-stop-shop for AI tools and resources. This central AI technical resource provides technical, legal, security and other support as needed to all employees to further efforts in AI adoption.

Learn more: AI orchestration: A beginner's guide for 2025

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Integrating AI into the human workforce

Even the most refined and technically sound AI agent solution will fall short of its full potential without a dedicated team of people that understand how to use them. Research shows that up to 120 million workers will need to be retrained or reskilled to effectively work with AI. As a result, there's a growing emphasis on AI education and workforce development among business leaders.

Surveying employees is a good way to assess their AI literacy and identify talent for upskilling or reskilling. There are many overlapping skills between AI and analytics, IT, and STEM fields, making workers with these faculties prime candidates for skill development. Hiring specialists is another option. Research shows AI is the fastest-growing job sector, with two-thirds of employers planning to hire talent with specific AI skills, such as prompt engineering.

Critical skills for the AI workplace include:

  • Technical competencies

  • AI fundamentals and capabilities

  • Data literacy and analysis

  • Tool proficiency

  • System monitoring and maintenance

However, even with these skills, the real challenge for employees is staying current and effective with each new wave of AI innovation. This requires a balance of technical know-how and human-centric skills, such as:

  • Adaptability and continuous learning

  • Critical thinking and problem-solving

  • Ethical decision-making

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Responsible development of the AI workforce

For all its potential, the AI workforce isn’t without its risks and ethical concerns. Businesses must consider the ethics of AI and establish governance for the AI workforce to ensure its compliance, security, and sustainability. 

Ethical considerations include:

  • ‍‍Transparency in decision-making: The logic of AI can be hard to track and understand, raising questions about accountability and explainability.

  • Biased outputs: Agents can reproduce biases in training data to the detriment of users and the organization’s reputation.

  • User privacy and data security: Agents may handle sensitive data, requiring robust security measures to safeguard against data breaches.

  • Balancing automation with human oversight: Establish control mechanisms that validate outputs for accuracy and compliance, planning for HITL and guardrails around sensitive or complex interactions.

A governance framework should include:

  • Defined roles, responsibilities, and engagement protocols for all stakeholders

  • Compliance measures for regulatory requirements

  • Ethical guidelines

  • Regular audits and assessments to ensure trust and safety

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Ready to create an AI workforce?

The AI workforce is set to reshape how work gets done across industries. It's poised to automate a range of complex tasks once reserved for humans, enhancing productivity, decision making, and innovation for business leaders. 

It may soon lead to the AI agent-to-agent economy (A2A) where agents transact, negotiate, and manage operations autonomously on behalf of businesses and consumers.

If you want to build and deploy AI agents for your unique application, Sendbird can help. Explore our AI agent platform or contact our team of AI experts to learn more.

And if you want to read further, you might enjoy these related resources:

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